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Автор: Grafiati

Опубліковано: 25 січня 2025

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Статті в журналах з теми "Multi-Robot task allocation (MRTA)"

Li, Ping, and Jun Yan Zhu. "The Application of Game Theory in RoboCup Soccer Game." Applied Mechanics and Materials 530-531 (February 2014): 1053–57. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.1053.

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In response to characters of multi-robot systems in RoboCup soccer game and dependence between decisions of robots, multi-robot systems task allocation was analyzed by means of game theory in this paper. Formalized description based on game theory for multi-robot system task allocation was offered, and a game theory based task allocation algorithm for multi-robot systems (GT-MRTA) was proposed. Experiments show that GT-MRTA has low complexity, and less time-consumption, can obtain comparative schemes with centralized method, and shows good robustness to communication failure and robot failure.

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Gul, Omer Melih. "Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks." Sensors 23, no. 6 (March 20, 2023): 3284. http://dx.doi.org/10.3390/s23063284.

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In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.

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Arif, Muhammad Usman, and Sajjad Haider. "A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking." ACM Transactions on Autonomous and Adaptive Systems 16, no. 1 (March 31, 2021): 1–23. http://dx.doi.org/10.1145/3502200.

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In a multi-robot operation, multi-tasking resources are expected to simultaneously perform multiple tasks, thus, reducing the overall time/energy requirement of the operation. This paper presents a task allocation framework named Rostam that efficiently utilizes multi-tasking capable robots. Rostam uses a task clustering mechanism to form robot specific task maps. The customized maps identify tasks that can be multi-tasked by individual robots and mark them for simultaneous execution. The framework then uses an Evolutionary Algorithm along with the customized maps to make quality task allocations. The most prominent contribution of this work is Rostam's flexible design which enables it to handle a range of task allocation scenarios seamlessly. Rostam's performance is evaluated against an auction-based scheme; the results demonstrate its effective use of multi-tasking robots. The paper also demonstrates Rostam's flexibility towards a number of MRTA scenarios through a case study.

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Yuan, Ruiping, Jiangtao Dou, Juntao Li, Wei Wang, and Yingfan Jiang. "Multi-robot task allocation in e-commerce RMFS based on deep reinforcement learning." Mathematical Biosciences and Engineering 20, no. 2 (2022): 1903–18. http://dx.doi.org/10.3934/mbe.2023087.

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<abstract><p>A Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order fulfillment system where multiple robots coordinate to complete a large number of order picking tasks. The multi-robot task allocation (MRTA) problem in RMFS is complex and dynamic, and it cannot be well solved by traditional MRTA methods. This paper proposes a task allocation method for multiple mobile robots based on multi-agent deep reinforcement learning, which not only has the advantage of reinforcement learning in dealing with dynamic environment but also can solve the task allocation problem of large state space and high complexity utilizing deep learning. First, a multi-agent framework based on cooperative structure is proposed according to the characteristics of RMFS. Then, a multi agent task allocation model is constructed based on Markov Decision Process. In order to avoid inconsistent information among agents and improve the convergence speed of traditional Deep Q Network (DQN), an improved DQN algorithm based on a shared utilitarian selection mechanism and priority empirical sample sampling is proposed to solve the task allocation model. Simulation results show that the task allocation algorithm based on deep reinforcement learning is more efficient than that based on a market mechanism, and the convergence speed of the improved DQN algorithm is much faster than that of the original DQN algorithm.</p></abstract>

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Martin, J. G., J. R. D. Frejo, R. A. García, and E. F. Camacho. "Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms." Intelligent Service Robotics 14, no. 5 (November 2021): 707–27. http://dx.doi.org/10.1007/s11370-021-00393-4.

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AbstractThe paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.

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Zhao, Donghui, Chenhao Yang, Tianqi Zhang, Junyou Yang, and Yokoi Hiroshi. "A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care." Machines 10, no. 8 (July 28, 2022): 622. http://dx.doi.org/10.3390/machines10080622.

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Roboticized nursing technology is a significant means to implement efficient elderly care and improve their welfare. Introducing multi-heterogeneous robot systems (MHRS) and sensor networks into a smart home is a promising approach to improve the safety and acceptability of elderly care services in daily life. Among them, the energy consumption and task planning of MHRS determine nursing safety, which is particularly important in the real nursing process. Therefore, we established a novel smart home for elderly care based on seven heterogeneous nursing robots, and proposed a multi-robot task allocation (MRTA) algorithm, considering execution time and energy consumption. The whole system efficiency makes up for the functional limitations and service continuity of traditional MHRS. To realize efficiently conducted multitasks, we established an architecture with centralized task allocation center, robot alliance layer and distributed execution layer for the MHRS. The self-organizing architecture contributes to overall task allocation, communication and adaptive cooperative control between different robots. Then, to clearly describe the continuous nursing process with multiple simultaneous demands and emergency tasks, we modeled the whole nursing process with continuity, multi-priority, and interpretability. A novel MRTA algorithm with a dynamic bidding mechanism was proposed. Comprehensive experiments showed that the proposed algorithm could effectively solve the three key problems of multi-priority tasks, multi-robot safe and adaptive cooperation, and emergency task call in the scene of elderly care. The proposed architecture regarding the smart home could be applied in nursing centers, hospitals, and other places for elderly care.

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Elfakharany, Ahmed, and Zool Hilmi Ismail. "End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System." Applied Sciences 11, no. 7 (March 24, 2021): 2895. http://dx.doi.org/10.3390/app11072895.

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In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (TAI), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.

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Hong, Le, Weicheng Cui, and Hao Chen. "A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics." Journal of Marine Science and Engineering 9, no. 8 (August 14, 2021): 879. http://dx.doi.org/10.3390/jmse9080879.

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As marine plastic pollution threatens the marine ecosystem seriously, the government needs to find an effective way to clean marine plastics. Due to the advantages of easy operation and high efficiency, autonomous underwater vehicles (AUVs) have been applied to clean marine plastics. As for the large-scale marine environment, the marine plastic cleaning task needs to be accomplished through the collaborative work of multiple AUVs. Assigning the cleaning task to each AUV reasonably and effectively has an essential impact on improving cleaning efficiency. The coordination of AUVs is subjected to harsh communication conditions. Therefore, to release the dependence on the underwater communications among AUVs, proposing a reliable multi-robot task allocation (MRTA) model is necessary. Inspired by the evolutionary game theory, this paper proposes a novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning. This novel model not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation. A novel optimization algorithm, equilibrium optimizer (EO), is adopted as the optimizer. The simulation results validate the correctness of the results achieved by EO and the applicability of the proposed model. At last, several valuable conclusions are obtained from the simulations on the three different assumed AUVs.

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Zhang, Zhenqiang, Sile Ma, and Xiangyuan Jiang. "Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms." Mathematics 10, no. 24 (December 12, 2022): 4714. http://dx.doi.org/10.3390/math10244714.

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Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.

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Gautier, Paul, and Johann Laurent. "DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)." International Journal of Robotic Computing 3, no. 1 (May 1, 2021): 69–98. http://dx.doi.org/10.35708/rc1870-126266.

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Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

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Більше джерел

Дисертації з теми "Multi-Robot task allocation (MRTA)"

Chakraa, Hamza. "Οptimisatiοn techniques fοr mοnitοring a high-risk industrial area by a team οf autοnοmοus mοbile rοbοts". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH29.

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Cette thèse explore le développement et la mise en œuvre d’algorithmes d’optimisation pour la surveillance de zones industrielles à l’aide d’une équipe de robots mobiles autonomes. Le travail de recherche se concentre sur l’allocation de tâches multi-robots (MRTA), où un plan de mission quasi-optimal doit être généré. Un nouveau modèle prenant en compte des robots et des tâches hétérogènes est proposé, utilisant des algorithmes génétiques (GA) et une méthode de recherche locale 2-Opt pour résoudre le problème. La thèse intègre également des stratégies d’évitement des collisions, qui deviennent nécessaires lorsqu’il y a beaucoup de robots et de tâches. Une solution locale de bas niveau gère de nombreuses situations de conflit pendant la mission, ce qui peut entraîner des retards. Par conséquent, une solution pour ce cas a été proposée en utilisant le clustering. En outre, nous évaluons les solutions proposées à l’aide d’expériences réelles qui incluent un algorithme basé sur la navigation pour résoudre les problèmes de collision. Les résultats démontrent la valeur de ces algorithmes dans l’optimisation de la répartition des tâches et de la planification des chemins pour les robots mobiles autonomes dans les environnements industriels, ouvrant la voie à une planification de mission plus efficace et à une sécurité accrue dans les environnements industriels
This thesis explores the development and implementation of optimisation algorithms for monitoring industrial areas using a team of autonomous mobile robots. The research focuses on Multi-Robot Task Allocation (MRTA), where a near-optimal mission plan must be generated. A novel model considering heterogeneous robots and tasks is proposed, using Genetic Algorithms (GA) and 2-Opt local search methods to solve the problem. The thesis also integrates collision avoidance strategies, which become necessary when there are many robots and tasks. A low-level local solution handles many conflict situations during the mission, which can cause delays. Therefore, a solution for this case was proposed using clustering. Furthermore, we evaluate the proposed solutions through real-world experiments including a navigation-based algorithm that addresses collision issues. The results demonstrate the value of these algorithms in optimising task allocation and path planning for autonomous mobile robots in industrial settings, paving the way for more efficient mission planning and enhanced safety in industrial environments

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Sarker, Md Omar Faruque. "Self-regulated multi-robot task allocation." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html.

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To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published generic rules for division of labour derived from the observation of ant,human and robotic social systems. The concrete form of these rules, the attractive field model (AFM), provides sufficient abstraction to local communication and sensing which is uncommon in existing MRTA solutions. This dissertation validates the effectiveness of AFM to address MRTA using two bio-inspired communication and sensing strategies: "global sensing - no communication" and "local sensing - local communication". The former is realized using a centralized communication system and the latter is emulated under a peer-topeer local communication scheme. They are applied in a manufacturing shop-floor scenario using 16 e-puck robots. A robotic interpretation of AFM is presented that maps the generic parameters of AFM to the properties of a manufacturing shopfloor. A flexible multi-robot control architecture, hybrid event-driven architecture on D-Bus, has been outlined which uses the state-of-the-art D-Bus interprocess communication to integrate heterogeneous software components. Based-on the organization of task-allocation, communication and interaction among robots, a novel taxonomy of MRTA solutions has been proposed to remove the ambiguities found in existing MRTA solutions. Besides, a set of domainindependent metrics, e.g., plasticity, task-specialization and energy usage, has been formalized to compare the performances of the above two strategies. The presented comparisons extend our general understanding of the role of information exchange strategies to achieve the distributed task-allocations among various social groups.

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Gage, Aaron. "Multi-robot task allocation using affect." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000465.

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Schneider, E. "Mechanism selection for multi-robot task allocation." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3018369/.

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There is increasing interest in fielding multi-robot teams for applications such as search and rescue, warehouse automation, and delivery of consumer goods. Task allocation is an important problem to solve in such multi-robot settings. Given a mission that can be decomposed into discrete tasks, the Multi-Robot Task Allocation (MRTA) problem looks for an assignment of tasks to robots that ultimately results in efficient execution of the mission. There is a range of approaches to this optimisation problem, from centralised solvers to fully distributed methods that involve no explicit coordination between team members. Somewhere in the middle of this range lie market-based approaches, where tasks can be treated as goods, robots as "buyers" who can compute and express their own preferences for tasks in a virtual marketplace, and some clearing mechanism exists to match tasks to robots according to these preferences. The most common type of market-based mechanism for multi-robot task allocation is an auction, in which tasks are announced to the team, robots compute and place bids that encode some measure of cost or utility of performing the tasks, and tasks are awarded to robots over a number of rounds, according to the particular rules of the mechanism. Many different auction mechanisms exist, and they vary in the trade-offs that they make between computation time and space on the one hand, and performance of the execution of the mission on the other. In addition, the performance that results from a mechanism's allocation can be greatly affected by properties of task environments---the spatial and temporal arrangements of tasks, as well as other properties like precedence constraints, whether tasks require the simultaneous cooperation of multiple robots, and so on---in which it is employed. A simple mechanism that is inexpensive to compute and scales well may perform well in some environments, but not in others. The work presented in this thesis focuses on this relationship between auction-based task allocation mechanisms and properties of task environments, with the goal of developing a method of selecting, from a portfolio, a mechanism that is appropriate for a given task environment. The first part of this work is an empirical performance evaluation of a range of mechanisms employed in a series of environments of increasing complexity. The second part of this work uses results from this evaluation to develop and train a data-driven method of mechanism selection using properties of environments that can be measured at the start of a mission. The results show that, under certain conditions, this method of mechanism selection can lead to significant performance improvements compared to using a single mechanism alone.

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Das, Gautham Panamoottil. "Task allocation strategies for multi-robot systems." Thesis, Ulster University, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667759.

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Sung, Cynthia Rueyi. "Data-driven task allocation for multi-robot deliveries." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84717.

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Анотація:

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 93-97).
In this thesis, we present a distributed task allocation system for a team of robots serving queues of tasks in an environment. We consider how historical information about such a system's performance could be used to improve future allocations. Our model is representative of a multi-robot mail delivery service, in which teams of robots would have to cooperate to pick up and deliver packages in an environment. We provide a framework for task allocation, planning, and control of the system and analyze task switching as a method for improving a task allocation as the system is running. We first treat a system where robots exchange tasks as they encounter each other in the environment. We consider both cases where the number of robots matches the number of task queues being served and where it does not. Most importantly, for situations where an optimal task switching policy would be too computationally expensive, we provide heuristics that nonetheless guarantee task completion. Our simulations show that our heuristics also generally lower the costs of task completion. We incorporate historical data about system performance by looking at a spatial allocation of tasks to robots in the system. We propose an algorithm for partitioning the environment into regions of equal workload for the robots. In order to overcome communication constraints, we introduce hubs, locations where robots can pass tasks to each other. We simulate the system with this additional infrastructure and compare its performance to that without hubs. We find that hubs can significantly improve performance when the task queues themselves follow some spatial structure.
by Cynthia Rueyi Sung.
S.M.

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AL-Buraiki, Omar S. M. "Specialized Agents Task Allocation in Autonomous Multi-Robot Systems." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41504.

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Анотація:

With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders. Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversified in terms of applications. But in order to achieve these goals, the individual agents forming cooperative robotic systems need to be specialized in what they can accomplish, while ensuring accuracy and preserving the ability to perform diverse tasks. This thesis addresses the problem of task allocation in swarm robotics in the specific context where specialized capabilities of the individual agents are considered. Based on the assumption that each individual agent possesses specialized functional capabilities and that the expected tasks, which are distributed in the surrounding environment, impose specific requirements, the proposed task allocation mechanisms are formulated in two different spaces. First, a rudimentary form of the team members’ specialization is formulated as a cooperative control problem embedded in the agents’ dynamics control space. Second, an advanced formulation of agents’ specialization is defined to estimate the individual agents’ task allocation probabilities in a dedicated specialization space, which represents the core contribution of this thesis to the advancement and practice in the area of swarm robotics. The original task allocation process formulated in the specialization space evolves through four stages of development. First, a task features recognition stage is conceptually introduced to leverage the output of a sensing layer embedded in robotic agents to drive the proposed task allocation scheme. Second, a matching scheme is developed to best match each agent’s specialized capabilities with the corresponding detected tasks. At this stage, a general binary definition of agents’ specialization serves as the basis for task-agent association. Third, the task-agent matching scheme is expanded to an innovative probabilistic specialty-based task-agent allocation framework to generalize the concept and exploit the potential of agents’ specialization consideration. Fourth, the general framework is further refined with a modulated definition of the agents’ specialization based on their mechanical, physical structure, and embedded resources. The original framework is extended and a prioritization layer is also introduced to improve the system’s response to complex tasks that are characterized based on the recognition of multiple classes. Experimental validation of the proposed specialty-based task allocation approach is conducted in simulation and on real-world experiments, and the results are presented and discussed in light of potential applications to demonstrate the effectiveness and efficiency of the proposed framework.

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KARAMI, HOSSEIN. "Task planning and allocation for multi-agent collaborative robot systems." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1083925.

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Анотація:

In this thesis we address the problem of human robot interaction in industrial environments from collaboration perspective. This thesis, in particular, focuses on introducing novel frameworks for coordination of heterogeneous teams made of humans and robots that collaboratively aim to reach to a common goal. In the last decade, robots has received enormous attentions for being employed in both industrial environments and workplaces. Thin is mainly because of a number of reasons: (I) the shift of mass production industries toward autonomous industry units, (II) huge amount of financial and scientific investments on robotics, and (III) substitution of humans with robots to accomplish hazardous and stressful tasks. However Due to the limited cognitive knowledge and reasoning of the robots in accomplishing complex operations, they still are not able to operate in a fully autonomous fashion and independently from their human counterparts. Therefore presence of human operators, as a complementary counterparts, in workplaces becomes fundamental for robots to become utterly safe, reliable and operative. The goal of this thesis is to design and implement a framework whereby humans and/or robots can together play a complementary role, while applying their individual skills to accomplish a task. Human-robot collaboration (HRC) is defined as the purposeful interaction among humans and robots in a shared space, and it is aimed at a common goal. The design of such framework for HRC problems, requires to satisfy many requirements from which flexibility, adaptability and safety, are the primary characteristics of such framework. In this thesis we mainly focus on multi-agent robot systems task allocation and planning. We consider two main aspects in defining our objectives: on one hand we investigate on HRC, and implement alternative frameworks to model and study collaborations in industrial scenarios considering various roles of humans in coordination and collaboration with robots. On the other hand, the presence of humans is neglected and it is assumed that robots are able to fully precept the environment independently from human cognitive support, as this can be the case of future where Artificial intelligence might substitute the skills of humans. To model a HRC scenario, a smart framework is required to start, coordinate and terminate a collaborative process. This framework, in particular has to be aware of agents and their types, determine their responsibilities and roles, be aware of their physical structure, define the logical relationship among the agent and handle the collaboration process fluently. In this thesis, to address the framework described above, we propose different frameworks and evaluate their effectiveness in solving HRC problems. To formulate task planning and allocation problem , we introduce and implement three variants of AND/OR graphs, namely, c-layer AND/OR graphs, Branched AND/OR graphs, and Iteratively deepened AND/OR graphs. The first two variants aim at addressing the problem of task allocation among humans and collaborative robots in object defect inspection (ODI) scenarios in HRC context. Instead, the third variant targets Task and motion planning (TAMP) problems for heterogeneous robots. TAMP problems, compared to HRC problem, is not only responsible for allocating task among agents at higher-level, but also at lower level, it plans motions for agents and ultimately, interconnects higher levels of task planning to lower levels of motion planning and control to achieve a complete planning framework. To validate the applicability and scalability of our proposed frameworks, we design and implement various real-world and simulation experiments and we also evaluate their effectiveness in terms of achieving desired objectives, and quantitatively with other available methods in the literature.

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Sun, Dali [Verfasser], and Bernhard [Akademischer Betreuer] Nebel. "Adaptive task allocation, localization and motion planning for the multi-robot system." Freiburg : Universität, 2017. http://d-nb.info/114157571X/34.

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Dutia, Dharini. "Multi-Robot Task Allocation and Scheduling with Spatio-Temporal and Energy Constraints." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1298.

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Анотація:

Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the structure of the environment; temporal constraints express task completion deadlines. There has been significant research in multi-robot task allocation involving spatio-temporal constraints. However, limited attention has been paid to combine them with team formation and non- instantaneous task execution time. We achieve team formation by including quota constraints which ensure to schedule the number of robots required to perform the task. We introduce and integrate task activation (time) windows with the team effort of multiple robots in performing tasks for a given duration. Additionally, while visiting tasks in space, energy budget affects the robots operation time. We map energy depletion as a function of time to ensure long-term operation by periodically visiting recharging stations. Research on task planning approaches which combines all these conditions is still lacking. In this thesis, we propose two variants of Team Orienteering Problem with task activation windows and limited energy budget to formulate the simultaneous task allocation and scheduling as an optimization problem. A complete mixed integer linear programming (MILP) formulation for both variants is presented in this work, implemented using Gurobi Optimizer and analyzed for scalability. This work compares the different objectives of the formulation like maximizing the number of tasks visited, minimizing the total distance travelled, and/or maximizing the reward, to suit various applications. Finally, analysis of optimal solutions discover trends in task selection based on the travel cost, task completion rewards, robot's energy level, and the time left to task inactivation.

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Частини книг з теми "Multi-Robot task allocation (MRTA)"

Zitouni, Farouq, and Ramdane Maamri. "FA-SETPOWER-MRTA: A Solution for Solving the Multi-Robot Task Allocation Problem." In Computational Intelligence and Its Applications, 317–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89743-1_28.

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Öztürk, Savaş, and Ahmet Emin Kuzucuoğlu. "Building a Generic Simulation Model for Analyzing the Feasibility of Multi-Robot Task Allocation (MRTA) Problems." In Modelling and Simulation for Autonomous Systems, 71–87. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43890-6_6.

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Koubaa, Anis, Hachemi Bennaceur, Imen Chaari, Sahar Trigui, Adel Ammar, Mohamed-Foued Sriti, Maram Alajlan, Omar Cheikhrouhou, and Yasir Javed. "General Background on Multi-robot Task Allocation." In Robot Path Planning and Cooperation, 129–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77042-0_6.

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Janati, Farzam, Farzaneh Abdollahi, Saeed Shiry Ghidary, Masoumeh Jannatifar, Jacky Baltes, and Soroush Sadeghnejad. "Multi-robot Task Allocation Using Clustering Method." In Advances in Intelligent Systems and Computing, 233–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_19.

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Schneider, Eric, Elizabeth I. Sklar, and Simon Parsons. "Mechanism Selection for Multi-Robot Task Allocation." In Towards Autonomous Robotic Systems, 421–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64107-2_33.

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Huang, Chuang, Hao Zhang, and Zhuping Wang. "Task Allocation of Multi-robot Coalition Formation." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control, 221–30. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_22.

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Tuck, Victoria Marie, Pei-Wei Chen, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto, S. Shankar Sastry, and Sanjit A. Seshia. "SMT-Based Dynamic Multi-Robot Task Allocation." In Lecture Notes in Computer Science, 331–51. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60698-4_20.

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Hawley, John, and Zack Butler. "Hierarchical Distributed Task Allocation for Multi-robot Exploration." In Springer Tracts in Advanced Robotics, 445–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32723-0_32.

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Munoz, Francisco, Ashutosh Nayak, and Seokcheon Lee. "Task Allocation in Multi-robot Systems—Resource Welfare." In Engineering Applications of Social Welfare Functions, 55–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20545-3_5.

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Aşık, Okan, and H. Levent Akın. "Effective Multi-robot Spatial Task Allocation Using Model Approximations." In RoboCup 2016: Robot World Cup XX, 243–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68792-6_20.

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Тези доповідей конференцій з теми "Multi-Robot task allocation (MRTA)"

Kashid, Sujeet, and Ashwin Dharmesh Kumat. "Hierarchically Decentralized Heterogeneous Multi-Robot Task Allocation System." In 2024 7th International Conference on Intelligent Robotics and Control Engineering (IRCE), 143–48. IEEE, 2024. http://dx.doi.org/10.1109/irce62232.2024.10739829.

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Dhanaraj, Neel, Jeon Ho Kang, Anirban Mukherjee, Heramb Nemlekar, Stefanos Nikolaidis, and Satyandra K. Gupta. "Multi-Robot Task Allocation Under Uncertainty Via Hindsight Optimization." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 16574–80. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611370.

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Ching-Wei, Chuang, and Lin Wei-Yu. "Task Decomposition and Multi-Robot Task Allocation in Exploration With Bayesian Networks." In 2024 Eighth IEEE International Conference on Robotic Computing (IRC), 80–83. IEEE, 2024. https://doi.org/10.1109/irc63610.2024.00018.

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Lee, Goeun, Donggil Lee, and Yoonseob Lim. "Task Allocation for Heterogeneous Multi-Robot Systems with Diverse Capabilities." In 2024 24th International Conference on Control, Automation and Systems (ICCAS), 1599–600. IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773254.

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Heppner, Georg, David Oberacker, Arne Roennau, and Rüdiger Dillmann. "Behavior Tree Capabilities for Dynamic Multi-Robot Task Allocation with Heterogeneous Robot Teams." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 4826–33. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610515.

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Calvo, Álvaro, and Jesús Capitán. "Optimal Task Allocation for Heterogeneous Multi-robot Teams with Battery Constraints." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 7243–49. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611147.

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Baccouche, Chaima, Imen Iben Ammar, Dimitri Lefebvre, and Achraf Jabeur Telmoudi. "A preliminary study about multi-robot task allocation with energy constraints." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 3057–62. IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711402.

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De La Rochefoucauld, Virgile, Simon Lacroix, Photchara Ratsamee, and Haruo Takemura. "Solving Multi-Robot Task Allocation and Planning in Trans-media Scenarios." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5764–69. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801394.

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Yan, Fuhan, and Kai Di. "Multi-robot Task Allocation in the Environment with Functional Tasks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/653.

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Анотація:

Multi-robot task allocation (MRTA) problem has long been a key issue in multi-robot systems. Previous studies usually assumed that the robots must complete all tasks with minimum time cost. However, in many real situations, some tasks can be selectively performed by robots and will not limit the achievement of the goal. Instead, completing these tasks will cause some functional effects, such as decreasing the time cost of completing other tasks. This kind of task can be called “functional task”. This paper studies the multi-robot task allocation in the environment with functional tasks. In the problem, neither allocating all functional tasks nor allocating no functional task is always optimal. Previous algorithms usually allocate all tasks and cannot suitably select the functional tasks. Because of the interaction and sequential influence, the total effects of the functional tasks are too complex to exactly calculate. We fully analyze this problem and then design a heuristic algorithm. The heuristic algorithm scores the functional tasks referring to linear threshold model (used to analyze the sequential influence of a functional task). The simulated experiments demonstrate that the heuristic algorithm can outperform the benchmark algorithms.

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Chuang, Ching-Wei, and Harry H. Cheng. "A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66902.

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Анотація:

Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.

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Звіти організацій з теми "Multi-Robot task allocation (MRTA)"

Lerman, Kristina, Chris Jones, Aram Galstyan, and Maja J. Mataric. Analysis of Dynamic Task Allocation in Multi-Robot Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459067.

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